Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 233

The DOI matches its Crossref record.

Energy Efficiency in Smart Buildings: IoT Approaches

Open access
Metallidou, Chrysi K., Psannis, Kostas E., Egyptiadou, Eugenia Alexandropoulou·2020·IEEE Access

Abstract

The Internet of Energy (IoE) impacts on smart cities' power sector. IoE is an implementation of the Internet of Things technology (IoT) into distributed energy systems and aims to achieve energy efficiency, to avoid energy wasting, and improve environmental conditions. IoE technology includes, among others, utilizing smart sensors and renewable energy integration. Therefore, the IoE is becoming a legal science tool to serve the purpose of a smart city. In this paper, we refer to the reasons that led the European Union to compile Regulations for facilitating transformation of existing cities, starting from existing buildings, into smart buildings. We propose a smart building template that manages the performance of all technical systems through IoT technology with the view of achieving energy efficiency. In addition, in order to improve the certification of existing buildings, as for energy performance, we propose an automated remote, control method supported by cloud interface. This method minimizes time consuming procedures and stores, on a cloud platform the energy performance of each building, for the purpose of drawing conclusion and applying measures.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/access.2020.2984461

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

See all 233

Metadata

Authors
Metallidou, C. K., Psannis, K. E., & Egyptiadou, E. A.
Venue
IEEE Access
Published
2020
Volume
8
Pages
63679-63699
ISSN
2169-3536 (ISSN-L)
OpenAlex
W3014640470
Cited by
233
References
0
FWCI
13.15
Type
journal article
Language
en
Open access
gold

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Energy Efficiency in Smart Buildings: IoT Approaches. https://app.cochat.ai/papers/10.1109/access.2020.2984461
The paper
Metallidou, C. K., Psannis, K. E., & Egyptiadou, E. A. (2020). Energy Efficiency in Smart Buildings: IoT Approaches. IEEE Access. https://doi.org/10.1109/access.2020.2984461

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.